1.3. Components of a Hyperspectral System

Hyperspectral imaging systems can be deployed on different platforms, selected based on the target application, spatial scale, and desired resolution:

• Spaceborne platforms including NASA’s EMIT sensor and Pixxel’s Firefly satellites are ideal for large-scale area monitoring. Pixxel’s constellation, for instance, delivers a 5-metre ground sampling distance (GSD), with a swath of 40 km, and fine spectral resolution, supporting monitoring at commercially viable scales.
• Airborne sensors such as AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and HYDICE (Hyperspectral Digital Imagery Collection Experiment) are used for regional, high-resolution studies. These sensors are typically flown on aircraft or drones for more targeted missions.
• UAV-mounted and handheld systems are popular for local-scale studies, offering flexibility and frequent deployment for agricultural monitoring, environmental assessments, and lab-based applications.

One of the defining differences between hyperspectral and other imaging technologies is its ability to go beyond appearance. Traditional cameras or even multispectral sensors might be able to tell you that a region is green and likely covered in vegetation, whereas hyperspectral sensors can reveal vegetation type, health and stress levels, moisture content, or the presence of disease or nutrient deficiency.

A Brief History of Spaceborne Hyperspectral Missions

The development of hyperspectral imaging in space has followed a steady trajectory from early exploratory and research-focused missions to today’s commercial systems.

Early Demonstrations (2000s):‍

• EO-1 Hyperion (2000, NASA): The first spaceborne hyperspectral sensor, capturing 220 spectral bands (400–2500 nm). While limited by spatial resolution (30 m) and narrow swath (7.7 km), it proved the feasibility of spaceborne HSI.
• CHRIS/PROBA (2001, ESA): Offered 18–62 bands with multi-angle viewing, enabling research into directional reflectance properties.

Second Generation (2010s):

• HICO (2009, aboard ISS): Focused on coastal and ocean environments, providing samples at 90 m with full spectral coverage (380 - 960 nm) and a very high signal-to-noise ratio.
• HysIS (2018, ISRO): India’s first dedicated hyperspectral satellite, capturing VNIR in 60 spectral bands and SWIR in 256 spectral bands, advancing operational use cases.

Contemporary Era (2020s):‍

• PRISMA (2019, ASI): Combined hyperspectral (400–2505 nm, 239 bands) with a panchromatic sensor for enhanced mapping.
• EnMAP (2022, DLR): Designed for global environmental monitoring with high spectral resolution (230 spectral bands) and a wide swath (30 km).
• EMIT (2022, NASA): An ISS-mounted hyperspectral mission mapping the mineral composition of arid dust source regions. Its data helps model dust impacts on Earth’s radiative balance and is openly distributed via NASA’s DAAC for global research use.
• Commercial constellations: Companies such as Pixxel are scaling HSI into operational networks, providing frequent revisits, higher spatial resolution, and large-scale coverage to support industries from agriculture to mining.

Data acquisition basics

Most hyperspectral systems operate as passive sensors, relying on sunlight reflected from Earth’s surface to capture information. This contrasts with active systems like LiDAR or Synthetic Aperture Radar (SAR), which emit their own energy and measure how it reflects back.

Because passive hyperspectral sensors depend on natural light, several factors influence data quality:

• Cloud cover,
• Time of day,
• Atmospheric conditions

As a result, careful acquisition planning and atmospheric correction are essential to ensure usable data.

Finally, like all remote sensing data, hyperspectral imagery must be preprocessed to correct for artefacts, calibration errors, and atmospheric distortions. Once refined, hyperspectral imagery becomes a powerful layer of intelligence, revealing spectral properties of the Earth’s surface.